The Application of Vibration Accelerations in the Assessment of Average Friction Coefficient of a Railway Brake Disc

Sawczuk, Wojciech

doi:10.1515/msr-2017-0016

Cited by 14 publications

(7 citation statements)

References 25 publications

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“…The results of the research showed that on the basis of the braking system vibration acceleration measures analyzed in this study, it is possible to both diagnose the condition of the brake, already described in [ 43 ], as well as to assess the braking process by determining the average coefficient of friction [ 44 ].…”

Section: Regression Model Of Friction Lining Wear and μ M Coefficient Variability Based On The Analysis mentioning

confidence: 99%

Evaluation of Wear of Disc Brake Friction Linings and the Variability of the Friction Coefficient on the Basis of Vibroacoustic Signals

Sawczuk

Ulbrich

Kowalczyk

et al. 2021

Sensors

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The article presents the results of friction and vibroacoustic tests of a railway disc brake carried out on a brake stand. The vibration signal generated by the friction linings provides information on their wear and offers evaluation of the braking process, i.e., changes in the average friction coefficient. The algorithm presents simple regression linear and non-linear models for the thickness of the friction linings and the average coefficient of friction based on the effective value of vibration acceleration. The vibration acceleration signals were analyzed in the amplitude and frequency domains. In both cases, satisfactory values of the dynamics of changes above 6 dB were obtained. In the case of spectral analysis using a mid-band filter, more accurate models of the friction lining thickness and the average coefficient of friction were obtained. However, the spectral analysis does not allow the estimation of the lining thickness and the friction coefficient at low braking speeds, i.e., 50 and 80 km/h. The analysis of amplitudes leads to the determination of models in the entire braking speed range from 50 to 200 km/h, despite the lower accuracy compared to the model, based on the spectral analysis. The vibroacoustic literature presents methods of diagnosis of the wear of various machine elements such as bearings or friction linings, based on amplitude or frequency analysis of vibrations. These signal analysis methods have their limitations with regard to their scope of use and the accuracy of diagnosis. There are no cases of simultaneous use of different methods of analysis. This article presents the simultaneous application of the amplitude and frequency methods in the analysis of vibroacoustic signals generated by brake linings. Moreover, algorithms for assessing the wear of friction linings and the average coefficient of friction were presented. The algorithm enables determination of the time at which the friction linings should be replaced with new ones. The final algorithm analyzes the vibration acceleration signals using both amplitude analysis for low braking speeds, as well as spectral analysis for medium and high braking speeds.

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Section: Regression Model Of Friction Lining Wear and μ M Coefficient Variability Based On The Analysis mentioning

confidence: 99%

Evaluation of Wear of Disc Brake Friction Linings and the Variability of the Friction Coefficient on the Basis of Vibroacoustic Signals

Sawczuk

Ulbrich

Kowalczyk

et al. 2021

Sensors

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“…The chiping of material affects the contact pressure and thus acts as a disturbance for uniform pressure build up between the lining/disc. During field operations, it is very difficult to deduce the contact pressure per contact point as the braking operation is very fast [15] The contact pressure is assumed to be uniform at every point along the lining/disc and as such influence the contact regime to be in a non-variable situation (Table 1).…”

Section: Data Development Modulementioning

confidence: 99%

A Valorized Scheme for Failure Prediction Using ANFIS: Application to Train Track Breaking System

Sparthan¹,

Nzie²,

Sohfotsing³

et al. 2020

OJAppS

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In the rolling stock sector, the ability to protect passengers, freight and services relies on heavy inborn maintenance. Initiating an accurate model suitable to foresee the change of attitude on components when operating rolling stock systems will assist in reducing lock down and favors heavy productivity. In that light, this paper showcases a suitable methodology to track degradation of components through the blinding of physic laws and artificial intelligent techniques. This model used to foresee failure deterioration rate and remaining useful life (RUL) speculation is case study to showcase its quality and perfection, within which behavioral data are obtained through simulated models initiated in Mathlab. For feature extraction and forecasting issues, different neuro-fuzzy inference systems are designed, learnt and authenticated with powerful outputs gained during this process.

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“…In past decades, various diagnostic methods, such as oil sample detection [ 4 ], acoustic emission [ 5 , 6 , 7 , 8 ], and vibration analysis [ 9 , 10 , 11 ], have been employed to diagnose rolling bearing faults, and the vibration signal based approach is seen as the most effective and convenient method. Numerous signal analysis methods have been researched to process bearing fault vibration signals for fault feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform

Pang

Tang

Tian

et al. 2018

Sensors

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When rolling bearing failure occurs, vibration signals generally contain different signal components, such as impulsive fault feature signals, background noise and harmonic interference signals. One of the most challenging aspects of rolling bearing fault diagnosis is how to inhibit noise and harmonic interference signals, while enhancing impulsive fault feature signals. This paper presents a novel bearing fault diagnosis method, namely an improved Hilbert time–time (IHTT) transform, by combining a Hilbert time–time (HTT) transform with principal component analysis (PCA). Firstly, the HTT transform was performed on vibration signals to derive a HTT transform matrix. Then, PCA was employed to de-noise the HTT transform matrix in order to improve the robustness of the HTT transform. Finally, the diagonal time series of the de-noised HTT transform matrix was extracted as the enhanced impulsive fault feature signal and the contained fault characteristic information was identified through further analyses of amplitude and envelope spectrums. Both simulated and experimental analyses validated the superiority of the presented method for detecting bearing failures.

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The Application of Vibration Accelerations in the Assessment of Average Friction Coefficient of a Railway Brake Disc

Cited by 14 publications

References 25 publications

Evaluation of Wear of Disc Brake Friction Linings and the Variability of the Friction Coefficient on the Basis of Vibroacoustic Signals

Evaluation of Wear of Disc Brake Friction Linings and the Variability of the Friction Coefficient on the Basis of Vibroacoustic Signals

A Valorized Scheme for Failure Prediction Using ANFIS: Application to Train Track Breaking System

Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform

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